Disclaimer: Apache Airflow is an effort undergoing incubation at The
Apache Software Foundation (ASF), sponsored by the Apache Incubator.
Incubation is required of all newly accepted projects until a further
review indicates that the infrastructure, communications, and
decision making process have stabilized in a manner consistent with
other successful ASF projects. While incubation status is not
necessarily a reflection of the completeness or stability of
the code, it does indicate that the project has yet to be fully
endorsed by the ASF.

Airflow is a platform to programmatically author, schedule and monitor
workflows.

Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks.
The airflow scheduler executes your tasks on an array of workers while
following the specified dependencies. Rich command line utilities make
performing complex surgeries on DAGs a snap. The rich user interface
makes it easy to visualize pipelines running in production,
monitor progress, and troubleshoot issues when needed.

When workflows are defined as code, they become more maintainable,
versionable, testable, and collaborative.

Airflow is not a data streaming solution. Tasks do not move data from
one to the other (though tasks can exchange metadata!). Airflow is not
in the Spark Streaming
or Storm space, it is more comparable to
Oozie or
Azkaban.

Workflows are expected to be mostly static or slowly changing. You can think
of the structure of the tasks in your workflow as slightly more dynamic
than a database structure would be. Airflow workflows are expected to look
similar from a run to the next, this allows for clarity around
unit of work and continuity.